A Novel Approach Based on Reinforcement Learning for Finding Global Optimum

Abstract

A novel approach to optimizing any given mathematical function, called the MOdified REinforcement Learning Algorithm (MORELA), is proposed. Al-though Reinforcement Learning (RL) is primarily developed for solving Mar-kov decision problems, it can be used with some improvements to optimize mathematical functions. At the core of MORELA, a sub-environment is gen-erated around the best solution found in the feasible solution space and com-pared with the original environment. Thus, MORELA makes it possible to discover global optimum for a mathematical function because it is sought around the best solution achieved in the previous learning episode using the sub-environment. The performance of MORELA has been tested with the re-sults obtained from other optimization methods described in the literature. Results exposed that MORELA improved the performance of RL and per-formed better than many of the optimization methods to which it was com-pared in terms of the robustness measures adopted.